@article{Burgin_2023,
  title        = {The European Nucleotide Archive in 2022},
  author       = {Burgin, Josephine and Ahamed, Alisha and Cummins, Carla and Devraj, Rajkumar and Gueye, Khadim and Gupta, Dipayan and Gupta, Vikas and Haseeb, Muhammad and Ihsan, Maira and Ivanov, Eugene and Jayathilaka, Suran and Balavenkataraman Kadhirvelu, Vishnukumar and Kumar, Manish and Lathi, Ankur and Leinonen, Rasko and Mansurova, Milena and McKinnon, Jasmine and O’Cathail, Colman and Paupério, Joana and Pesant, Stéphane and Rahman, Nadim and Rinck, Gabriele and Selvakumar, Sandeep and Suman, Swati and Vijayaraja, Senthilnathan and Waheed, Zahra and Woollard, Peter and Yuan, David and Zyoud, Ahmad and Burdett, Tony and Cochrane, Guy},
  year         = 2023,
  month        = {Jan},
  journal      = {Nucleic acids research},
  volume       = 51,
  number       = {D1},
  pages        = {D121–D125},
  doi          = {10.1093/nar/gkac1051},
  issn         = {0305-1048},
  url          = {http://dx.doi.org/10.1093/nar/gkac1051},
  abstractnote = {The European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena), maintained by the European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI), offers those producing data an open and supported platform for the management, archiving, publication, and dissemination of data; and to the scientific community as a whole, it offers a globally comprehensive data set through a host of data discovery and retrieval tools. Here, we describe recent updates to the ENA's submission and retrieval services as well as focused efforts to improve connectivity, reusability, and interoperability of ENA data and metadata.},
  language     = {en}
}
@article{Choudhary_2019,
  title        = {pysradb: A Python package to query next-generation sequencing metadata and data from NCBI Sequence Read Archive},
  author       = {Choudhary, Saket},
  year         = 2019,
  month        = {Apr},
  journal      = {F1000Research},
  volume       = 8,
  pages        = 532,
  doi          = {10.12688/f1000research.18676.1},
  issn         = {2046-1402},
  url          = {http://dx.doi.org/10.12688/f1000research.18676.1},
  abstractnote = {The NCBI Sequence Read Archive (SRA) is the primary archive of next-generation sequencing datasets. SRA makes metadata and raw sequencing data available to the research community to encourage reproducibility and to provide avenues for testing novel hypotheses on publicly available data. However, methods to programmatically access this data are limited. We introduce the Python package, pysradb, which provides a collection of command line methods to query and download metadata and data from SRA, utilizing the curated metadata database available through the SRAdb project. We demonstrate the utility of pysradb on multiple use cases for searching and downloading SRA datasets. It is available freely at https://github.com/saketkc/pysradb.},
  keywords     = {GEO; NCBI; NGS; SRA; bioinformatics; metadata},
  language     = {en}
}
@article{Katz_2022,
  title        = {The Sequence Read Archive: a decade more of explosive growth},
  author       = {Katz, Kenneth and Shutov, Oleg and Lapoint, Richard and Kimelman, Michael and Brister, J. Rodney and O’Sullivan, Christopher},
  year         = 2022,
  month        = {Jan},
  journal      = {Nucleic acids research},
  volume       = 50,
  number       = {D1},
  pages        = {D387–D390},
  doi          = {10.1093/nar/gkab1053},
  issn         = {0305-1048},
  url          = {http://dx.doi.org/10.1093/nar/gkab1053},
  abstractnote = {The Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra/) stores raw sequencing data and alignment information to enhance reproducibility and facilitate new discoveries through data analysis. Here we note changes in storage designed to increase access and highlight analyses that augment metadata with taxonomic insight to help users select data. In addition, we present three unanticipated applications of taxonomic analysis.},
  language     = {en}
}
